Spain COVID19 Mortality Rate
ggplot(US) + geom_line(aes(x=Reported,y=Rate)) +
scale_y_continuous(labels = scales::percent) +
labs(title="Spain COVID19 Mortality Rate ",x="Date Reported",y="Mortality Rate") +ylim(0,0.6)
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Plot of Daily Cases and Deaths
daily_cases <-ggplot(US) + geom_line(aes(x=Reported,y=Cases,col="Daily Cases")) +
labs(title="COVID-19 Cases by Date") +
geom_line(aes(x=Reported,y=MA14,col="Mov AVg")) +
geom_point(aes(x=Reported,y=Cases))
daily_deaths <-ggplot(US) + geom_line(aes(x=Reported,y=Deaths,col="Daily Deaths")) +
labs(title="COVID-19 Deaths by Date") + ylim(0,1000) +
geom_line(aes(x=Reported,y=MAD,col="Mov AVg")) +
geom_point(aes(x=Reported,y=Deaths))
ggplotly(daily_cases)
ggplotly(daily_deaths)
USA <- subset(US,Reported >="2020-06-01")
ggplot(USA) + geom_line(aes(x=Reported,y=Cases,col="Daily Cases")) +
labs(title="COVID-19 Cases by Date since Jun. 1, 2020") +
geom_line(aes(x=Reported,y=MA14,col="Mov AVg")) +
geom_point(aes(x=Reported,y=Cases))

ggplot(USA) + geom_col(aes(x=Reported,y=Deaths,col="Daily Deaths")) +
labs(title="COVID-19 Deaths by Date (since Jun. 1, 2020)") + ylim(0,200) +
geom_line(aes(x=Reported,y=MAD,col="Mov AVg"))
## Warning: Removed 4 rows containing missing values (position_stack).

Non-Moving Average By Week and By Month
US$Monthly <- as.Date(cut(US$Reported,
breaks = "month"))
US$Weekly <- as.Date(cut(US$Reported,
breaks = "week",
start.on.monday = FALSE))
Weekly_Cases <- aggregate(Cases~Weekly,US,FUN=sum)
Weekly_Deaths <- aggregate(Deaths~Weekly,US,FUN=sum)
Weekly_Cases$DRate <- Weekly_Deaths$Deaths/Weekly_Cases$Cases
Weekly_Cases$LivedSaved <- Weekly_Cases$Cases * (max(Weekly_Cases$DRate) - Weekly_Cases$DRate)
ggplot(Weekly_Cases) + geom_col(aes(x=Weekly,y=Cases)) +
labs(title="Weekly Cases",x="Date Reported", y="Weekly Cases")

ggplot(Weekly_Deaths) + geom_col(aes(x=Weekly,y=Deaths)) +
labs(title="Weekly Deaths",x="Date Reported", y="Weekly Deaths") +
ylim(0,6000)

Monthly Cases and Deaths
Monthly_Cases <- aggregate(Cases~Monthly,US,FUN=sum)
Monthly_Deaths <- aggregate(Deaths~Monthly,US,FUN=sum)
Monthly_Cases$DRate <- Monthly_Deaths$Deaths/Monthly_Cases$Cases
Monthly_Cases$LivedSaved <- Monthly_Cases$Cases * (max(Monthly_Cases$DRate) - Monthly_Cases$DRate) * 100
ggplot(Monthly_Cases) + geom_col(aes(x=Monthly,y=Cases)) +
labs(title="Monthly Cases")

ggplot(Monthly_Deaths) + geom_col(aes(x=Monthly,y=Deaths)) +
labs(title="Monthly Deaths")
